Machine vision workloads are complex, and their performance requirements often present challenges in areas like latency, security, energy use and reliability. Hierarchal partitioning of those workloads often makes sense, where the machine vision software is split into multiple stages (for example, contrast enhancement, feature extraction, object recognition, threat detection), which are run at different layers of the [intelligent camera -> edge node -> MEC -> cloud] hierarchy.
This talk will introduce the hierarchal cloud - edge architecture, and discuss the properties and capabilities of its many layers. It will propose an example segmentation of machine vision algorithms, and investigate the tradeoffs of how we can map them onto the various layers of processing available in the hierarchy. Finally, it will look at the dual flows of model training and inference for AI applications, and discuss which portions of those flows make sense in different edge layers, and how they can be secured, orchestrated and managed.
